Objective This article aims at explaining national medal totals at the 1992–2016 Summer Olympic Games (n = 1,289 observations) and forecasting them in 2016 (based on 1992–2012 data) and 2020 with a set of variables similar to previous studies, as well as a regional (subcontinents) variable not tested previously in the literature in English. Method Econometric testing not only resorts to a Tobit model as usual but also to a Hurdle model. Results Most variables have a significant impact on national team medal totals; it appears to be negative for most regions other than North America except Western Europe and Oceania (not significant). Then, two models (Tobit and Hurdle) are implemented to forecast national medal totals at the 2016 and 2020 Summer Olympics. Conclusion Both models are complementary for the 2016 forecast. The 2020 forecast is consistent with Olympic Medals Predictions, although some striking differences are found.
L’analyse des déterminants de la performance olympique fut longtemps une macroéconomie de guerre froide où le nombre de médailles gagnées par une nation est expliqué par ses ressources économiques et humaines, puis par son régime politique et le fait d’être le pays hôte des Jeux. On propose une vision des Jeux d’après guerre froide, recherchant un complément d’explication plus proche de l’idéal olympique qui tiendrait compte des performances individuelles, de la culture et des disciplines sportives. Les estimations économétriques correspondant à cette vision renouvelée sont limitées par la faible disponibilité des données, portant sur la période 1976-2004. Dans une première spécification inspirée de l’article de référence de la macroéconomie des médailles par nations, avec une classification plus précise des pays, le PIB par tête, la population, le régime politique et l’effet pays hôte déterminent le nombre de médailles gagnées. Une deuxième spécification ajoute une variable capturant des différences culturelles par régions du monde qui améliore l’estimation précédente. Une troisième estimation utilise une nouvelle base de données individuelles et introduit une classification économique des disciplines sportives parmi les variables explicatives. Elle permet d’estimer les chances, pour un athlète d’une nation donnée, d’atteindre une finale olympique ainsi que ses chances de gagner une médaille. Pour la prévision des médailles gagnées à Pékin 2008, on introduit une variable inertielle dans le modèle macroéconomique, captant le culte de l’olympisme des nations habituées à gagner des médailles et les différenciant des autres nations participantes. On prévoit aussi ces gains à partir de données individuelles, sans inertie.
Multinational companies (MNCs) based in 26 post-communist transition economies (PTEs) emerged during the 1990s. Their outward foreign direct investment (OFDI) boomed dramatically from 2000 to 2007 in these countries, and then muddled through ence is revealed in a sample of 15 PTEs for which data are available from 2000 to 2015. Most of these economies appear to be on the brink of moving from the second to the third stage of Dunning's investment development path. The geographical distribution of their OFDI favors host countries located in other PTEs, developed market economies, and tax havens while their industrial structure is more concentrated on services rather than on manufacturing and the primary sector. PTE-based MNCs primarily adopt a strategy of market-seeking OFDI. Econometric testing shows that push factors are major determinants of OFDI. The results demonstrate that OFDI is determined by the home country's level of economic development, the size of its home market, and its rate of growth as well as technological variables: OFDI decreases with an increase in the number of scientists in the home economy and with an increase in the share of high-tech products in overall exports, exhibiting a negative technological gap. A lagged relationship between OFDI and previous inward FDI suggests that Mathews' linkage-leverage-learning theory is relevant in the case of PTEs.
International audienceThe analysis of international trade in sports goods is still in its infancy. In order to alleviate the sports economics ignorance in this area, an entirely new dataset is built up by extracting Comtrade data at the most disaggregated level (6 digits). The dataset covers 41 countries, 36 different sports goods, and 94-96% of global sports goods trade (1994-2004). The country sample is divided into five regional areas: North American Free Trade Area (NAFTA), EU + Switzerland, Eastern Europe, Asia and other emerging countries. A detailed snapshot of global trade in sports goods and its distribution by major areas, countries and products provides first empirical evidence about how much industrialisation in emerging countries and de-industrialisation in developed market economies have affected international specialisation, and indirectly tests multinational companies outsourcing and production relocation strategies in low unit cost countries in the sports goods industry. Then, studying export/import ratios and country's position in the global market, it appears that major trading areas are Asia, Europe and NAFTA. Major exporters are China, Hong Kong, the USA and France, and major importers are the USA, Japan, Germany, France, the UK and Italy. The biggest market shares are in sportswear, anoraks and gymnastic equipment trade. Asia, Eastern Europe and emerging countries have an excess balance in sports goods trade, whereas NAFTA and Europe are in deficit. Three indexes assess a country's comparative advantages and disadvantages and competitiveness, and describe international specialisation. NAFTA and Europe are specialised in equipment-intensive sports goods, while Asia, Eastern Europe and emerging countries are specialised in trite sports goods and some less equipment-intensive sports goods. NAFTA is not competitive in any sport good, Europe is competitive in skis, emerging countries and Eastern Europe in sportswear and anoraks, and Asia in sportswear, anoraks, rackets, balls, skates and gymnastic equipment. Such an international specialisation pattern fits with both assumptions of industrialisation/de-industrialisation and firms outsourcing strategies. A principal component analysis with hierarchical ascendant classification groups trite sports goods as opposed to intensive-equipment sports goods in global trade and shows that production relocation influences international trade specialisation. Major policy implications are that developed economies and multinational companies should continue investing in R&D in order to keep their comparative advantages in equipment-intensive sports goods, while Asian and emerging countries should more tightly supervise working conditions and child labour in their subcontracting producers that work for foreign multinational companies
International audienceAn econometric model with very significant explanatory variables of Olympic Games medal wins is emended in such a way as to explain the qualification of national football teams to the semi-finals of the FIFA World Cup. It is shown that this model is not able to predict 100% of semi-finalists of the next Cup
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